For the following two exercises, assume that you have access to a computer program or Internet source that can generate a list of zeros and ones of any desired length. Pseudorandom number generators (PRNGs) play an important role in simulating random noise in physical systems by creating sequences of zeros and ones that appear like the result of flipping a coin repeatedly. One of the simplest types of PRNGS recursively defines a random- looking sequence of N integers a₁, a₂...., ay by fixing two special integers K and M and letting an+1 be the remainder after dividing K.a, into M, then creates a bit sequence of zeros and ones whose nth term b,, is equal to one if an, is odd and equal to zero if an is even. If the bits b, are pseudorandom, then the behavior of their average (b₁+ b₂+ +bNYN should be similar to behavior of averages of truly randomly generated bits. 65. [T] Starting with K = 16,807 and M = 2,147,483,647, using ten different starting values of a₁, compute sequences of bits b, up to n = 1000, and compare their averages to ten such sequences generated by a random bit generator.
For the following two exercises, assume that you have access to a computer program or Internet source that can generate a list of zeros and ones of any desired length. Pseudorandom number generators (PRNGs) play an important role in simulating random noise in physical systems by creating sequences of zeros and ones that appear like the result of flipping a coin repeatedly. One of the simplest types of PRNGS recursively defines a random- looking sequence of N integers a₁, a₂...., ay by fixing two special integers K and M and letting an+1 be the remainder after dividing K.a, into M, then creates a bit sequence of zeros and ones whose nth term b,, is equal to one if an, is odd and equal to zero if an is even. If the bits b, are pseudorandom, then the behavior of their average (b₁+ b₂+ +bNYN should be similar to behavior of averages of truly randomly generated bits. 65. [T] Starting with K = 16,807 and M = 2,147,483,647, using ten different starting values of a₁, compute sequences of bits b, up to n = 1000, and compare their averages to ten such sequences generated by a random bit generator.
Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
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![For the following two exercises, assume that you have
access to a computer program or Internet source that can
generate a list of zeros and ones of any desired length.
Pseudorandom number generators (PRNGs) play an
important role in simulating random noise in physical
systems by creating sequences of zeros and ones that
appear like the result of flipping a coin repeatedly. One of
the simplest types of PRNGs recursively defines a random-
looking sequence of N integers a₁, a2...., ay by fixing
two special integers K and M and letting an+1 be the
remainder after dividing K.a, into M, then creates a bit
sequence of zeros and ones whose nth term b, is equal to
one if an is odd and equal to zero if an, is even. If the bits
b, are pseudorandom, then the behavior of their average
(b₁ + b₂+ +bNYN should be similar to behavior of
averages of truly randomly generated bits.
65.
[T] Starting with K = 16,807 and
M = 2,147,483,647, using ten different starting values
of a compute sequences of bits bn up to n = 1000,
and compare their averages to ten such sequences generated
by a random bit generator.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb35f0eab-2452-4d20-90d7-c62e398afc80%2F047ff207-2f39-4049-b6c1-5934c15bba18%2Fb5r18j4g_processed.png&w=3840&q=75)
Transcribed Image Text:For the following two exercises, assume that you have
access to a computer program or Internet source that can
generate a list of zeros and ones of any desired length.
Pseudorandom number generators (PRNGs) play an
important role in simulating random noise in physical
systems by creating sequences of zeros and ones that
appear like the result of flipping a coin repeatedly. One of
the simplest types of PRNGs recursively defines a random-
looking sequence of N integers a₁, a2...., ay by fixing
two special integers K and M and letting an+1 be the
remainder after dividing K.a, into M, then creates a bit
sequence of zeros and ones whose nth term b, is equal to
one if an is odd and equal to zero if an, is even. If the bits
b, are pseudorandom, then the behavior of their average
(b₁ + b₂+ +bNYN should be similar to behavior of
averages of truly randomly generated bits.
65.
[T] Starting with K = 16,807 and
M = 2,147,483,647, using ten different starting values
of a compute sequences of bits bn up to n = 1000,
and compare their averages to ten such sequences generated
by a random bit generator.
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